Dynamic Integration of Distributed Generators and Electric Vehicles for Smart Distribution System Operation Using Dragonfly and PUMA Algorithms

This work proposes a dynamic technique that incorporates the use of the Dragonfly Algorithm (DA) and PUMA Optimisation (PO) to combine electric vehicles (EVs) and distributed generators (DGs) in smart distribution systems. Two typical benchmark systems, i.e., IEEE 33-bus and IEEE 69-bus, are evaluated in different operating conditions. Although EVs would be aligned by means of three charging infrastructures, like residential, workplace, and public EV charging stations (EVCS), DGs are aligned and optimally placed to minimize the power loss and improve the voltage stability index (VSI). In order to consider the variability of loads and the reversibility of energy flows, 24-h dynamic vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations are being modelled. The simulation results demonstrate that using DGs in combination with smart EV charging decreases technical losses and increases voltage stability, or reduces CO2 emissions and operating costs, compared to charging separately. In comparison to the other method of optimization, the Dragonfly Algorithm reduces overall losses of energy more effectively and more convergently. The authors believe the outcomes indicate that the active operation of renewable DGs and EVs can contribute a considerable amount to enhancing the technical, economic, and environmental viability of contemporary models of power distribution.